Clustering is a well known concept that refers to a group of individual computer systems, or nodes, operating together as if it were a single system or single computing resource. One characteristic of a cluster is its scalability, or the ability to add (or delete) nodes from the cluster to meet the user's performance requirements. Another characteristic of a cluster is high availability (HA). High availability refers to the ability of a server to remain operational, or available to a user, even when one or more nodes in the cluster fail. High availability can be improved in a cluster by implementing “failover” procedures that enable the operations performed on a node to failover to, or be assumed by, another node in the cluster in the event that that node fails. Failover procedures must include a policy for selecting the node(s) to assume the tasks or applications running on the failed node. Some failover policies improve the high availability of a cluster system more than others. Thus, the implementation of a failover policy can be crucial when considering the overall high availability of a cluster.
Establishing a failover policy is a simple task when the cluster consists of only two nodes. When one node fails, the only possible solution is to failover all applications running on the failed node to the surviving node. When there are more than two nodes in the cluster, and multiple applications running on each node, however, the failover possibilities become numerous. For example, theoretically all applications running on the failed node can failover to any one of the remaining nodes, and similarly any of the several applications running on the failed node can failover to any one of the surviving nodes. It is apparent that as the number of nodes and number of applications running on each node increase, the failover possibilities increase dramatically. Establishing concrete failover policies is critical for any cluster environment, as the high availability of the cluster will depend on it.
Many cluster systems operating on Windows NT based servers utilize Microsoft Cluster Services (MSCS) software to provide high availability for the cluster. In the Advanced Server product, MSCS provides fail-over capability for a two-node system. All applications running on the first node failover to the second node, and vice versa. This is considered to be the trivial case. In the DataCenter Server product, MSCS provides fail-over capability for up to a four-node system. MSCS multi-node fail-over is slightly more complex, but is encompassed within the prior art that is described more fully below.
Some known failover policies do exist that address failover in multiple node systems. One method, commonly referred to as cascading, establishes a circular list for all nodes in the cluster. For example, if there are four nodes (nodes 1–4) in the cluster, failure of node 1 will cause all applications running on node 1 to failover to node 2, failure of node 2 will cause all applications running on node 2 to failover to node 3, failure of node 3 will cause all applications running on node 3 to failover to node 4, and failure of node 4 will cause all applications running on node 4 to failover to node 1. The cascading failover policy can be represented graphically by the following illustration, where the direction of the arrow points to the node that will assume responsibility for all applications running on the failed node.

In the above failover policy, each node in the cluster may failover only to one single node that has been designated prior to the time of failure. Further, all applications running on any failed node must failover to the same designated surviving node.
Another known failover policy enables applications running on any given node to failover to any remaining node in the cluster, as is depicted by the following graph.

Although the applications of a failed node may theoretically failover to any surviving node, a single failover path must be chosen by a system administrator or the like when the cluster is established or at another time well in advance of the time of failure. Thus, although the possibility exists to select any node for failover purposes, the selection must take place in advance, and there is no way to dynamically assess the best suited node at the time the failure occurs.
Disadvantages of the above-described failover policies are many. First, for any given node, a failover node must be designated in advance. The obvious disadvantages of this are that either failover nodes are designated with complete disregard to the resources needed by the failed node and those available at the failover node (as in the cascading failover policy), or must be determined in a manner that cannot take into account changes in system resources that have occurred since the failover designations were made. For example, additional applications may be added to nodes, or user demands for any given application may increase over time, or even at any given time over the course of any given day. Further, in each of the failover policies described above all applications running on a failed node failover to another single node. This may impact the high availability of the system if the resources of a failover node at the particular time needed are such that it cannot handle all applications, but could otherwise provide failover for certain ones of those applications.
Another known failover policy utilizes a separate “passive” node that is present in the cluster exclusively for the purpose of being the failover node for all active nodes in the cluster. As illustrated in the following graph, each node on the cluster that is actively running applications (nodes 1–3) fails over to node 4, which is not tasked with running any applications other than in the event of a failover.
The disadvantages described above also are present in this failover policy. A further disadvantage is that this failover policy designates only a single failover node for each node running applications in the cluster, and requires the presence of an otherwise idle node, which is an inefficient use of system resources.
It is apparent from the above discussion of known failover policies in a cluster environment that there presently is no known way to dynamically choose among several possible failover nodes at the time failure actually occurs. Thus, none of these known policies enable the system to select a failover node that necessarily will have adequate, or the most available, resources at the time the failure occurs. Further, there is no known method by which the applications running on a failed node may be allocated to different ones of surviving nodes. A failover policy having one or more of the above features would be advantageous in that it would enable optimization in failover designations. Evaluation at the time of failure of the resources available on each surviving node, and directing failover to nodes that are most capable of handing one or more of the applications of the failed node, would enable more efficient use of cluster resources, and improve high availability of the cluster.